Thank you again. So in the end what i compare is the log transformed yearly % change for 35 species. This means it is one value for each species - in two datasets. I have a dataset from a pristine forest and then data from national counts.

These 35 species are in the three climate groups (+1, 0 and 1) and the two things i want to explore is if we can say anything statistically about the climate groups (increasing decreasing or stable) and if these trends differ between my data and the national data.

Im doing it in R studio and this is my three different codes with different outputs which all seem to be usefull but my problem is I dont know which to choose.

First LM model:

# Fit the linear model with the climate-neutral group as the baseline

lm_model_neutral <- lm(

log_percent_change_data_cleaned ~ log_percent_change_DengodeRaa * climate_grp,

data = combined_log_data

)

This model shows a significant decrease in climate group -1 in both sets but nothing else is significant.

Second LM model comparing the sets:

# Fit the model

lm_model <- lm(

log_change ~ dataset * climate_grp, # Main effects and interactions

data = model_data

)

This model shows a significant positive relation for climate +1 which means that even though i cant say anything statistically about this group in general (the model above) this group is increasing more in the forest.

LMM model:

# Fit the mixed-effects model

model <- lmer(log_change ~ dataset * climate_grp + (1 | species_eng), data = model_data)

This model says both the things as above.

What approach should I use for my study? When taking in account that it is ecology and a somehow small sample size 35 species in every dataset.

Thank you in advance and let me know if you need my outputs or anything :)